The kernel
The weighted sampling frame: typed weights with conservation invariants, strata for provenance, weighted accounting, entity structure, and a rules-engine adapter protocol so the tax-benefit model is swappable.
open-source population infrastructure
A nation is millions of households.
We build a synthetic one that stands in for them all.
populace is a stack for constructing weighted synthetic populations from public survey and administrative data — realistic enough to model tax and benefit policy for everyone, private by construction, and improved in the open.
every point above is a synthetic household · brightness ∝ survey weight
01 — the idea
The sampling frame, made executable.
Every population estimate rests on a frame: the list of units a sample is drawn from, and the weights that scale them back up to a country. In most pipelines that frame is implicit — scattered across data files, weight columns, and convention. When the convention breaks, the numbers break silently.
populace makes the frame a first-class datatype. Entity tables — people, households, tax units — with explicit links, typed weights that can never be silently zeroed, and a record of where every row came from. Imputation, calibration, and policy simulation are operators on that one object. The structure is built once and never re-derived.
02 — the stack
Three operators on one frame.
Conditional models
Weight-aware imputation by construction. Regime-gated, chained quantile forests draw missing variables from the weighted conditional distribution — no silent unweighted defaults, the bug class that has quietly distorted survey data for years.
Representation
The one place weights become representative. Sparse calibration with L0 pruning turns a large, diverse pool of candidate records into a population that hits thousands of administrative targets at once.
generation owns support · calibration owns representation
03 — the sources
Where every layer comes from.
04 — releases
The current data, read live.
Every release publishes its manifests, calibration diagnostics, and reform validation next to the data. The rows below read the release registry directly, so this page cannot go stale: latest is the newest published build; certified is the build pinned as the policyengine.py default.
latest
certified
Browse every target on the calibration dashboard · releases on Hugging Face
05 — evidence
It already matches the data it aims to replace.
PolicyEngine's enhanced Current Population Survey is the microdata behind millions of US policy calculations. The published populace-US release — built entirely from primary sources (the incumbent is the benchmark, never an input), with full variable parity — beats it on training, held-out, and full-surface loss in the matched-sample, symmetric-refit comparison.
training loss
0.18vs 1.09
lower is better
held-out loss
0.04vs 0.32
739 unseen targets
full-surface loss
0.21vs 1.41
all 3,704 targets
build populace-us-2024-5da5a95 · 2026-06-11 · matched 41,314 households, symmetric refit. Per individual target the incumbent still wins more often (2,528 of 3,704 to our 1,127) — we win big where we win and lose narrowly where we lose. Net short-term capital gains land on the signed PUF-anchored target (−$77.4B), and every donor is a primary survey; every remaining gap is itemized on the calibration dashboard. The two populations share an open-source unit-construction engine, so this measures synthesis quality on a partly shared scaffold. We report the gaps, not just the wins.
Read the L0 calibration paper · Read the dynamics design paper
06 — the commons
Toward one faithful record per person.
The long-run goal is a communal population that many parties improve — at full scale, one statistically faithful record for every person, carrying no one's private data. Contributions come in three forms, and they are exactly the three operators: records as new strata, conditional models trained on data a contributor holds, and facts as calibration targets.
A contribution merges only if it improves the population's score on held-out, rotating evidence without degrading any protected family. Privacy is enforced by provenance and measurement, not by blurring: public sources can be sharp, private evidence enters only through certified models, and the population must resemble held-out data — never anyone's training data.
- → records · a new stratum at honest weights
- → conditionals · certified P(y | x), never microdata
- → facts · targets with standard errors